Machine Learning Engineering MEAP

✍ Scribed by Ben Wilson

• Year: 2021
• Tongue: English
• Leaves: 123
• Category: Library

✦ Table of Contents

Machine Learning Engineering MEAP V02
Copyright
Welcome letter
Brief contents
Chapter 1: What is a Machine Learning Engineer?
1.1 Why ML Engineering?
1.2 The core components of ML Engineering
1.2.1 Planning
1.2.2 Scoping & Research
1.2.3 Experimentation
1.2.4 Development
1.2.5 Deployment
1.2.6 Evaluation
1.3 The goals of ML Engineering
1.4 Summary
Chapter 2: Your Data Science could use some Engineering
2.1 Augmenting a complex profession with processes to increase success in project work
2.2 A foundation of simplicity
2.3 Co-opting principles of Agile software engineering
2.4 The foundation of ML Engineering
2.5 Summary
Chapter 3: Before you model: Planning and Scoping a project
3.1 Planning: you want me to predict what?!
3.1.1 Basic planning for a project
Assumption of business knowledge
Assumption of Data Quality
Assumption of functionality
Curse of knowledge
Analysis Paralysis
3.1.2 That first meeting
3.1.3 Plan for demos. Lots of demos.
3.1.4 Experimentation by solution building: wasting time for pride’s sake
3.2 Experimental Scoping: Setting expectations and boundaries
What is experimental scoping?
3.2.1 Experimental scoping for the ML team - Research
3.2.2 Experiment scoping for the ML team – Experimentation
Why is scoping a research (experiment) phase so important?
How much work is this going to be, anyway?
3.3 Summary
Chapter 4: Before you model: Communication and Logistics of projects
4.1 Communication: defining the problem
4.1.1 Understanding the problem
What do you want it to do?
What does the ideal end-state look like?
Who is your champion for this project that I can work with on building these experiments out?
When should we meet to share progress?
4.1.2 Critical discussion boundaries
Post-research phase discussion (Update meeting)
Post-experimentation phase (SME / User Acceptance Testing(UAT) review)
Development Sprint Reviews (Progress reports for a non-technical audience)
MVP review (Full demo with UAT)
Pre-production review (Final Demo with UAT)
4.2 Don’t waste our time: critical meetings with cross-functional teams
4.2.1 Experimental update meeting (Do we know what we’re doing here?)
4.2.2 SME review (prototype review / can we solve this?)
4.2.3 Development Progress Review(s) (is this thing actually going to work?)
4.2.4 MVP review (did you build what we asked for?)
4.2.5 Pre-prod review (We really hope we didn’t screw this up)
4.3 Setting limits on your experimentation
4.3.1 Set a time limit
4.3.2 Can you put this into production? Would you want to maintain it?
4.3.3 TDD vs RDD vs PDD vs CDD for ML projects
Test Driven Development (TDD) or Feature Drive Development (FDD)
Prayer Driven Development (PDD)
Chaos Driven Development (CDD)
Resume Driven Development (RDD)
4.4 Business rules chaos
4.4.1 Embracing chaos by planning for it
4.4.2 Human in the loop design
4.4.3 What’s your backup plan?
4.5 How to talk about results
4.6 Summary